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Haritha Koya, Product Manager at Cisco, exemplifies how modern product leadership hinges on turning analytics into strategy. By combining hands-on SQL expertise, prioritization frameworks like RICE, and clear KPI-to-outcome alignment, she builds data-driven roadmaps that improve usability, streamline operations, and deliver measurable business value.
The role of a product manager is evolving, demanding a data-informed approach to strategy. Effective leaders translate vast streams of data into clear roadmaps that drive business outcomes, requiring a blend of technical depth and strategic insight to ground decisions in evidence.
Haritha Koya, a Product Manager at Cisco Systems Inc., exemplifies this modern approach. Her work involves digging directly into data to uncover opportunities and steer development toward tangible results, offering a window into how hands-on analytics is becoming indispensable.
The primary challenge in data-driven product management is converting raw metrics into a coherent narrative. Data must be linked to business goals, providing a clear line from analytics to value while prioritizing what truly matters.
Koya emphasizes that this process begins with strategic context. “I start by connecting data to the underlying business objectives—whether it’s revenue growth, retention, or operational efficiency,” she explains. She uses established product prioritization frameworks to structure these insights for stakeholders.
Once identified, insights must be communicated effectively. Koya uses frameworks like the RICE scoring model to ensure balanced decisions. “The goal is always to turn numbers into a story that drives action, not just reports,” she adds.
Data insights can reshape a roadmap entirely. Combining quantitative analytics with qualitative feedback reveals unexpected user behaviors and critical usability issues, a synergy that catalyzes meaningful product improvements.
In one project, Koya’s team assumed low feature usage was due to a lack of awareness, but the data told a different story. “By digging into the analytics and combining them with user feedback, we identified a usability issue that was blocking progress,” Koya notes. This discovery prompted a pivot toward product redesign.
Acting on this insight led to a redesigned workflow that validated the adjustment. “The result was a 30% increase in successful completions and a 25% reduction in support tickets,” she states. This shows how data science in product management can uncover root causes and guide interventions.
For data to be effective, it must be accessible and understandable. Well-designed dashboards are critical to empowering stakeholders without overwhelming them, aligning with best practices for self-service analytics.
Koya’s approach is guided by utility. “I focus on three principles: relevance, simplicity, and actionability,” she says. This philosophy ensures every element serves a function, helping users grasp key patterns and making platforms like Looker or Tableau valuable assets.
The goal of any reporting system is to drive behavior. “I design for action, highlighting areas that need attention and providing context so users know what steps to take next,” Koya explains, ensuring dashboards effectively guide users.
Product management relies on both data and intuition, especially in ambiguous situations. Effective leaders balance these forces, using data to challenge assumptions and intuition to guide inquiries, especially when the two conflict, as reconciling incongruous findings can lead to deeper insights.
Koya views these moments as opportunities for investigation. “I treat intuition and data as complementary. When they don’t align, I first dig into the data to understand limitations,” she says.
She uses a methodical process to bridge the gap. “This approach lets me make informed decisions quickly while still being evidence-driven,” she states. By synthesising quantitative and qualitative evidence, managers can build a more resilient strategy.
Product managers with hands-on technical skills have an advantage in a data-rich environment. Koya’s skills allow her to move beyond surface-level reports. “My hands-on SQL and analytics skills give me a significant advantage because I can directly access and interpret the data,” she states.
This direct access enables a proactive understanding of product performance. Koya notes that this depth helps her spot bottlenecks not visible in high-level metrics. “Essentially, technical depth helps me make faster, more evidence-driven decisions,” she concludes.
While structured reporting tracks known metrics, ad-hoc analysis often uncovers transformative insights. Exploring data without a predefined hypothesis can reveal hidden behaviors that challenge existing strategies and can help refine customer churn prediction models.
A recent analysis led Koya to a strategic pivot. “I ran an ad-hoc analysis on feature usage and discovered that a small segment of power users was driving the majority of engagement,” she recalls.
This discovery prompted an immediate shift in priorities. “Based on the analysis, we pivoted to prioritize onboarding improvements and targeted education for the wider audience,” she says. This experience shows how analysis can reshape strategy, sometimes by predicting future customer needs from current data.
Defining KPIs is fundamental, but distinguishing between short-term activity and long-term outcomes is critical for sustainable value. This requires tying every KPI back to core business goals, and prioritization frameworks are essential in maintaining this focus.
Koya makes a clear distinction between outputs and outcomes. “I ensure KPIs tie to long-term outcomes by linking every metric to the underlying business goals and customer value,” she explains.
“I distinguish between activity metrics—what teams do day-to-day—and outcome metrics—the impact those actions have,” she continues. She complements quantitative data with qualitative feedback, a practice supported by mixed research synthesis designs.
The role of data in product management is increasingly integral to real-time strategy. Advanced analytics and AI-driven tools empower product managers to validate hypotheses with greater speed, marking a fundamental shift in the practice, with Generative Business Intelligence (Gen BI) accelerating this trend.
“Data is becoming central to enterprise product management—not just for reporting, but for shaping strategy, prioritization, and real-time decision-making,” Koya observes.
To stay ahead, product managers must expand their toolkit. “I’m excited to explore advanced analytics tools, AI-driven insights, and tighter integrations between product and customer data,” Koya adds. This highlights the new era of insights made possible by AI-powered business intelligence.
Ultimately, insights from professionals like Koya show that success relies on the disciplined processes guiding technology. By blending technical depth with a focus on business value, product leaders can navigate complexity and deliver meaningful results.
